Microsoft Professional Program in AI is a comprehensive, career-focused training program designed to help professionals develop practical skills in Artificial Intelligence, Machine Learning, data analysis, and AI-powered technologies within the Microsoft ecosystem. The program provides a structured learning path that connects fundamental AI concepts with practical tools, techniques, and real-world applications.
Duration: 5 days – 35 hrs
Overview
The Microsoft Professional Program in AI is an intensive, career-focused training program designed to develop the practical knowledge and technical capabilities needed to design, build, and apply Artificial Intelligence (AI) and Machine Learning (ML) solutions. The program combines essential AI theory with hands-on development, enabling participants to progress from foundational concepts to practical implementation using Python, machine learning frameworks, and Microsoft Azure AI technologies.
Participants will begin by establishing a strong foundation in AI concepts, mathematical principles, statistics, and data preparation, providing the knowledge required to understand how intelligent systems learn from data. The program then progresses into supervised and unsupervised machine learning, feature engineering, model evaluation, and optimization using practical datasets and industry-relevant scenarios.
The training also covers advanced AI areas including deep learning, computer vision, Natural Language Processing (NLP), reinforcement learning, and intelligent agents. Through guided exercises and practical projects, participants will explore how neural networks can be developed for image recognition, text analysis, prediction, classification, and intelligent decision-making.
A key component of the program is the application of AI through Microsoft Azure, where participants will learn how cloud-based AI services can support model development, deployment, scalability, and integration into real-world applications. Learners will gain an understanding of how AI models can move beyond experimentation toward practical solutions that can be deployed and consumed by applications and users.
The program also emphasizes Responsible AI, addressing important considerations such as fairness, bias, privacy, security, transparency, explainability, and ethical decision-making. Participants will learn why responsible practices are essential when developing and deploying AI systems in professional and organizational environments.
By the end of the program, participants will have developed a broader understanding of the AI development lifecycle, from data preparation and model development to evaluation, deployment, and responsible implementation. They will be equipped to apply AI techniques to real-world challenges and continue advancing toward professional roles in AI development, machine learning, data science, cloud AI, and intelligent application development.
Learning Objectives
- Understand the fundamental concepts and principles of artificial intelligence.
- Apply machine learning techniques to solve real-world problems.
- Design and implement AI algorithms using popular frameworks and tools.
- Utilize deep learning algorithms for image recognition, natural language processing, and other tasks.
- Implement intelligent agents and reinforcement learning algorithms.
- Develop AI solutions that can leverage cloud platforms and services.
- Apply ethical considerations and responsible AI practices in their work.
Audience
- Professionals aspiring to become AI experts or AI developers.
- Data scientists and machine learning practitioners seeking to enhance their AI skills.
- Software developers interested in expanding their knowledge in AI technologies.
- IT professionals looking to transition into AI-related roles.
- Anyone with a keen interest in AI and its applications.
Pre- requisites
- Basic programming knowledge (preferably Python)
- Familiarity with mathematical concepts (linear algebra, calculus, probability, and statistics)
Course Content
Module 1: Introduction to Artificial Intelligence
- Overview of AI concepts and terminology
- History and evolution of AI
- Ethical considerations in AI
Module 2: Essential Mathematics and Statistics for AI
- Linear algebra and calculus for AI
- Probability and statistics in AI applications
- Data normalization and feature scaling
Module 3: Machine Learning with Python and Azure
- Introduction to machine learning algorithms
- Supervised and unsupervised learning techniques
- Feature selection and dimensionality reduction
- Hands-on exercises using Python and Azure ML
Module 4: Deep Learning and Neural Networks
- Neural network fundamentals and architectures
- Convolutional neural networks (CNNs) for computer vision
- Recurrent neural networks (RNNs) for natural language processing
- Transfer learning and model optimization
Module 5: Reinforcement Learning and Intelligent Agents
Introduction to reinforcement learning
Markov decision processes and Q-learning
Building intelligent agents for decision-making
Hands-on reinforcement learning projects
Module 6: Natural Language Processing (NLP)
- NLP fundamentals and applications
- Text preprocessing and feature extraction
- Sentiment analysis and text classification
- Language generation and machine translation
Module 7: AI in the Cloud
- Leveraging cloud platforms for AI solutions
- Azure AI services and cognitive APIs
- Deploying AI models as web services
- Scalable and distributed AI computing
Module 8: Responsible AI and Ethical Considerations
- Bias and fairness in AI algorithms
- Privacy and security in AI applications
- Explainability and interpretability of AI models
- Responsible AI frameworks and guidelines
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